A survey of fecal virome and bacterial community of the diarrhea-affected cattle in northeast China reveals novel disease-associated ecological risk factors

ABSTRACT Limited information on the virome and bacterial community hampers our ability to discern systemic ecological risk factors that cause cattle diarrhea, which has become a pressing issue in the control of disease. A total of 110 viruses, 1,011 bacterial genera, and 322 complete viral genomes were identified from 70 sequencing samples mixed with 1,120 fecal samples from 58 farms in northeast China. For the diarrheic samples, the identified virome and bacterial community varied in terms of composition, abundance, diversity, and geographic distribution in relation to different disease-associated ecological factors; the abundance of identified viruses and bacteria was significantly correlated with the host factors of clinical status, cattle type, and age, and with environmental factors such as aquaculture model and geographical location (P < 0.05); a significant interaction occurred between viruses and viruses, bacteria and bacteria, as well as between bacteria and viruses (P < 0.05). The abundance of SMB53, Butyrivibrio, Facklamia, Trichococcus, and Turicibacter was significantly correlated with the health status of cattle (P < 0.05). The proportion of BRV, BCoV, BKV, BToV, BoNoV, BoNeV, BoAstV, BEV, BoPV, and BVDV in 1,120 fecal samples varied from 1.61% to 12.05%. A series of significant correlations were observed between the prevalence of individual viruses and the disease-associated ecological factors. A genome-based phylogenetic analysis revealed high variability of 10 bovine enteric viruses. The bovine hungarovirus was initially identified in both dairy and beef cattle in China. This study elucidates the fecal virome and bacterial community signatures of cattle affected by diarrhea, and reveals novel disease-associated ecological risk factors, including cattle type, cattle age, aquaculture model, and geographical location. IMPORTANCE The lack of data on the virome and bacterial community restricts our capability to recognize ecological risk factors for bovine diarrhea disease, thereby hindering our overall comprehension of the disease’s cause. In this study, we found that, for the diarrheal samples, the identified virome and bacterial community varied in terms of composition, abundance, diversity, configuration, and geographic distribution in relation to different disease-associated ecological factors. A series of significant correlations were observed between the prevalence of individual viruses and the disease-associated ecological factors. Our study aims to uncover novel ecological risk factors of bovine diarrheal disease by examining the pathogenic microorganism-host-environment disease ecology, thereby providing a new perspective on the control of bovine diarrheal diseases.

a total of 1,016 diarrheic samples and 104 healthy samples were randomly collected from 58 farms in Heilongjiang Province of China.The virome and bacterial commun ity of the collected samples were identified and analyzed using metagenomic technol ogy.After examining the key ecological risk factors of bovine diarrheic disease, the etiological characteristics of the common bovine enteroviruses were revealed, includ ing their prevalence, mixed infection, synergistic pathogenicity, genetic evolution, and cross-species transmission.This study aims to uncover novel ecological risk factors of bovine diarrheic disease by examining the pathogenic microorganism-host-environmen tal disease ecology, thereby providing a theoretical basis for comprehensive prevention and control strategies of bovine diarrheic disease, as well as a reference for the research of human and other animal diarrheic diseases.

Overview of virome and bacterial community
1,120 fecal samples that were randomly collected from 58 farms were pooled into 72 samples.The 72 pooled samples included the 62 pooled samples from 1,016 diarrheic cattle and the 10 pooled samples from 104 healthy cattle.The detailed information regarding the pooled samples is available in Table S1.Disease ecology information regarding 1,016 diarrheic samples and 104 healthy samples is shown in Table 1.The virome of the 72 pooled samples was identified by using the Illumina NovaSeq 6000 system.Results indicated that, of the 70 samples that were successfully sequenced, a total of 39 virus families, 86 virus genera, and 110 virus species were identified.In diarrheic samples, the number of virus families, genera, and species identified was 38, 82, and 102, respectively, while the healthy samples revealed 25 virus families, 37 virus genera, and 41 virus species.These data were shown in Table S2.Out of 70 mixed sequencing samples, a total of 322 complete or nearly complete viral genomes were identified; 208 of them (64.6%)belonged to RNA viruses and the remaining 114 (35.4%) were classified as DNA viruses.A total of 191 bovine diarrhea-associated viruses, 79 other eukaryotic viruses, and 52 Microviridae have been identified.At the family level, a total of 72 whole-genome sequences of Astroviridae, 53 of Coronaviridae, 52 of Microvir idae, 51 of Genomoviridae, 36 of Caliciviridae, 32 of Picornaviridae, 11 of Circoviridae, 9 of Dicistroviridae, 5 of Reoviridae, and 1 of Flaviviridae were identified, respectively (Table S3).The abundance of viruses at the family level is shown in Fig. 1.A total of 39 virus families were identified in all samples, among which 38 virus families were identified in diarrhea fecal samples; the top 10 viral families with the highest abundance were Coronaviridae, Picornaviridae, Reoviridae, Astroviridae, Betaflexiviridae, Siphoviridae, Myoviridae, Dicistroviridae, Microviridae, and Virgaviridae.Twentyfive viral families were detected in the health samples, and the top 10 viral families with the highest abundance were Coronaviridae, Caliciviridae, Genomoviridae, Siphoviridae, Astroviridae, Totiviridae, Podoviridae, Partiviridae, Virgaviridae, and Herpesviridae (Fig. 1a).Analysis of the virome at the family level in diarrheic and healthy fecal samples from different cities (regions), aquaculture models, and breeds revealed varying abundances and compositions.Among the 12 cities (regions) in Heilongjiang Province, cattle fecal samples from Heihe City revealed the most abundant virome composition, with a total of 25 viral families identified.Coronaviridae, Picornaviridae, Betaflexiviridae, Reoviridae, and Microviridae were the top five viral families with the highest abundance.Cattle fecal samples from Daqing City revealed the simplest virome composition, with a total of 7 viral families identified.Betaflexiviridae, Picornaviridae, Astroviridae, Reoviridae, and Coronaviridae were the top five viral families with the highest abundance (Fig. 1b).Results of studying the virome composition in different aquaculture models indicated that 31 viral families were detected in diarrhea fecal samples in intensive farming conditions, the Picornaviridae, Reoviridae, Coronaviridae, Microviridae, and Caliciviridae were the top five viral families with the highest abundance.Two viral families were detected in healthy fecal samples, including Caliciviridae and Coronaviridae.Thirty-two viral families were detected in diarrhea fecal samples in non-intensive farming condi tions, the Coronaviridae, Dicistroviridae, Myoviridae, Picornaviridae, and Siphoviridae were the top five viral families with the highest abundance.Seven viral families were detected in the healthy samples, the Coronaviridae, Genomoviridae, Siphoviridae, Baculoviridae, and Dicistroviridae were the top five viral families with the highest abundance.Results of studying the virome composition in different cattle breeds indicated that 34 viral families were detected in diarrhea fecal samples from Simmental cattle, the Coronaviridae, Dicistroviridae, Picornaviridae, Myoviridae, and Siphoviridae were the top five viral families 3a).Under different aquaculture models, the Shigella, Subdoligranulum and Enterococ caceae_Enterococcus showed high abundance in cattle diarrhea fecal samples under intensive aquaculture model, and Acinetobacter, Planomicrobium, and Streptococcus showed high abundance in cattle diarrhea fecal samples under non-intensive aquacul ture model (Fig. 3b and d).Psychrobacter, Acinetobacter, Planomicrobium, Arthrobacter, and Carnobacterium showed high abundance in multiple samples of diarrhea fecal samples from non-intensive aquaculture model.Shigella, Collinsella, and Bifidobacterium showed high abundance in both diarrheic and healthy fecal samples (Fig. 3e).Of the various types of cattle, Shigella, Enterococcaceae_Enterococcus, Subdoligranulum, and Erysipelotrichaceae_Clostridium showed high abundance in dairy cattle diarrhea fecal samples.Planomicrobium and SMB53 showed high abundance in beef cattle diarrhea feces, while Shigella, Collinsella, Enterococcaceae_Enterococcus, Subdoligranulum, and Butyricicoccus only exist in dairy cattle diarrhea fecal samples (Fig. 3c and f).Plano microbium, Carnobacterium, and Acinetobacter showed high abundance in the fecal samples of beef cattle with diarrhea.Among the healthy fecal samples of beef cattle, Trichococcus, Psychrobacter, Actinomyces, Collinsella, Shigella, and Bifidobacterium showed high abundance in several samples (Fig. 3g).
Variations in the relative abundance of virome at the family level and bacterial community at the genus level can be observed in cattle diarrheic fecal samples depending on the longitude and latitude.The analysis of different geographical locations showed that the abundance of Reoviridae increased with the increase of longitude in virome analysis, with the highest in Jixi City (longitude 130.97°E).The abundance of Polydnaviridae, Microviridae, and Picornaviridae increased with the increase of latitude, with the highest in Heihe City (latitude 50.24°N) (Fig. 4a).Bacterial community analysis showed that the abundance of Psychrobacter was high in all cities (regions), with the highest in Heihe City (latitude 50.24°N, longitude 127.49°E).The abundance of Shigella was high in Jixi City (latitude 45.30°N, longitude 130.97°E) (Fig. 4b).

Correlation analysis of the fecal virome-bacterial community of cattle affected by diarrhea with various environmental and host conditions
In order to further reveal the viruses or bacteria that were correlated with diarrhea affected cattle with various environment and host conditions, a correlation analysis of the virome and bacterial community was performed in fecal samples from diarrhea affected cattle by using regression analysis, a correlation heatmap, a chord diagram analysis, and a network association diagram.Binary logistic regression and multiple linear regression were used to analyze the correlation between virome and bacterial community and different host factors and environmental factors.The virome regression analysis showed that, under different host factors and environmental factors, there were significant differences in the abundance of virome at the family level in cattle fecal samples.Under different clinical statuses, the abundance of Picornaviridae, Coronaviridae, Caliciviridae, and Anelloviridae was significantly correlated with the clinical statuses (P < 0.05).Among them, the abundance of Picornaviridae, Coronaviridae, and Caliciviridae was significantly correlated with diarrhea (P < 0.05) (Fig. 5a).Under different aquaculture models, the abundance of Picornaviridae and Astroviridae in diarrhea fecal samples was significantly correlated with intensive aquaculture model (P < 0.05), and the abundance of Circoviridae was significantly correlated with non-intensive aquaculture model (P < 0.05) (Fig. 5b).In different types of cattle, the abundance of Herpesviridae and Circovir idae was significantly correlated with that of beef cattle (P < 0.05) (Fig. 5c).Under different age factors of cattle, the abundance of Astroviridae, Reoviridae, Siphoviridae, Myoviridae, Dicistroviridae, and Baculoviridae was significantly correlated with the age of cattle (P < 0.05), of which, the abundance of Astroviridae and Reoviridae was signif icantly correlated with calves (P < 0.05) (Fig. 5d).In different geographical locations, the abundance of Dicistroviridae was significantly correlated with longitude (P < 0.05) (Fig. 5e).The abundance of Totiviridae, Flaviviridae, and Circoviridae was significantly correlated with latitude (P < 0.05) (Fig. 5f).The bacterial community regression analysis showed that, under different host factors and environmental factors, there were also significant differences and abundance of bacterial community at the genus level in cattle fecal samples.Under different clinical statuses, the abundance of SMB53, Butyrivibrio, Facklamia, Trichococcus, and Turicibacter was significantly correlated with health (P < 0.05) (Fig. 5g).In different aquaculture models, the abundance of Corynebacterium and Trichococcus in diarrhea fecal samples was significantly correlated with the non-intensive aquaculture model (P < 0.05) (Fig. 5h).Among the different cattle types, the abun dance of Faecalibacterium, SMB53, Trichococcus, Planomicrobium, and Acinetobacter was significantly correlated with the cattle type (P < 0.05), and the abundance of Faecalibac terium was significantly correlated with the dairy cow (P < 0.05).The abundance of SMB53, Trichococcus, Planomicrobium, and Acinetobacter was significantly correlated with beef cattle (P < 0.05) (Fig. 5i).Under different age factors of cattle, the abundance of  Faecalibacterium and Dorea was significantly correlated with calves (P < 0.05), and the abundance of SMB53, Butyrivibrio, Jeotgalicoccus, Facklamia, Corynebacterium, Trichococ cus, Arthrobacter, Planomicrobium, and Acinetobacter was significantly correlated with adult cattle (P < 0.05) (Fig. 5j).In different geographical locations, the abundance of Pediococcus was significantly correlated with longitude (P < 0.05) (Fig. 5k).The abun dance of Arthrobacter and Planomicrobium was significantly correlated with latitude (P < 0.05) (Fig. 5l).
In this study, 191 complete genome sequences of 10 bovine enteric viruses were identified.In order to determine the genetic evolution of the identified virus sequences, phylogenetic tree based on the whole-genome sequence was constructed.The results showed that all 10 bovine enteric viruses showed genetic diversity, and multiple viruses showed coevolution of multiple host animals, with potential for cross-species transmis sion.The phylogenetic analysis of BKV showed that, except for BKV5/2021/CHN, which was divided into a small branch, all identified BKV sequences, as well as the sheep kobuvirus (GenBank accession number: GU245693) and the ferret kobuvirus (GenBank accession number: KF006985), aggregated to form the aichivirus B group (Fig. 11a).In order to further clarify the genetic evolution of BKV5/2021/CHN, sequence alignment, and Bayesian analysis were performed on the identified BKV strains.The BKV genome identified in this study and the deduced amino acid (aa) sequence were compared with the sequences of seven representative kobuvirus species previously reported.The results showed that the nucleotide sequence of BKV5/2021/CHN gene was the most similar to Kagoshima-2-24-KoV of aichivirus D group, with 81.1% homology and 50.7-66.7%homology with other kobuvirus sequences.BKV5/2021/CHN strain and aichivirus D2 strain 2-24 KoV have the highest amino acid homology (85.7%), and the homology of different proteins was 69.0% (P1), 97.9% (2C), and 96.1% (3CD), respectively (Table S8).The amino acid sequence alignment of 14 BKV strains identified in this study showed that the three key amino acid patterns KDELR, YGDD, and FLKR located in the 3D region were highly conservative.The amino acid motif of HWAL located in 2A region was mutated into HWAI, and NCTHFV was mutated into NCTTFV.Point mutations were also found in the nucleic acid binding region of the picornavirus helicase at positions 131-138 in the 2C region (Fig. S1a).Next, the Bayesian phylogenetic tree based on BKV5/2021/CHN VP1 sequence was reconstructed.The results showed that the genetic relationship between BKV5/2021/CHN and Kagoshima-2-24-KoV/2015/JPN (GenBank accession number: LC055960) was the closest in this study, and it was aggregated with Kagoshima-1-22-KoV/2014/JPN (GenBank accession number: LC055961) to form aichivirus D group (Fig. S1b).Phylogenetic analysis of BToV strains showed that different species of torovirus aggregated into different branches.This study identified that BToV strains were divided into three different branches (Fig. 11b).According to the phylogenetic analysis of BoNoV, the BoNoV identified in this study belonged to GIII type.BoNoV20/2021/CHN has the closest relationship with the Jena strain (Gen Bank accession number: AJ011099).BoNoV17/2021/CHN was separately aggregated into a branch.Other BoNoV strains identified in this study aggregated to form three different branches (Fig. 11c).In addition, the phylogenetic analysis of BoPV showed that BoPV16/2021/CHN and Bo-11-39/2009/JPN strains (GenBank accession number: LC006971) aggregated to form a branch.BoPV11/2021/CHN strain formed a branch independently.Other BoPV strains identified in this study gathered to form a large branch (Fig. 11d).The BCoV identified in this study was divided into two branches, of which BCoV2/2021/CHN, BCoV4/2021/CHN, BCoV7/2021/CHN strains, and SWUM/ NMG-D10/2020 (GenBank accession number: MW711287) converged to form one. BCoV1/2021/CHN, BCoV3/2021/CHN, BCoV5/2021/CHN, and BCoV6/2021/CHN strains had close genetic relationship with BCoV China/SWUN/A1/2018 (GenBank accession number: MN982198) and BCoV-China/SWUN/A10/2018 (GenBank accession number: MN982199), and gathered to form another branch (Fig. 11e).The phylogenetic analy sis of BoAstV showed that the strains identified in this study could be divided into five branches.Among them, the BoAstV47/2021/CHN strain was closely related to the BoAstV/JPN/Hokkaido12-25/2009 (GenBank accession number: LC047793), and was closely related to the BoAstV2/2021/CHN strain, the porcine astrovirus AstV5-US-1A122 (GenBank accession number: JX556693), and PAstVB5-AH29-2014 (GenBank accession number: MT642595), forming a branch that was far from the genetic relationship with other identified BoAstV strains (Fig. 11f).The phylogenetic analysis of BoNeV showed that the identified BoNeV strains gathered to form three branches (Fig. 11g).Based on the VP4 and VP7 genes of the BRV strain, a phylogenetic tree was constructed to analyze its genetic evolution.) and dromedary enterovirus 20 CC strain (GenBank accession number: KP345888) gathered to form another large branch (Fig. 11j).

Phylogenetic analysis of other eukaryotic viruses
In the current study, 79 other eukaryotic virus genomes were found in dairy cow fecal samples, including 8 Picornaviridae, 9 Dicistroviriade, 51 Genomoviridae, and 11 Circoviridae.Phylogenetic analysis of Picornaviridae showed that six of the sequences belonged to bovine hungarovirus and two belonged to picorna-like virus (Fig. 12a).Among them, six Hungarian viruses came from cattle fecal samples from Hegang City, Qiqihar City, Harbin City, Heihe City, and Mudanjiang City.Two picorna-like viruses came from cattle fecal samples in Qiqihar and Heihe.The Picornavirida strains identified in this study belong to a branch together with viruses from other hosts.Phylogenetic analysis of Dicistroviriade showed that nine Dicistroviriade viruses belong to a cluster, namely, the Cripavirus group (Fig. 12b).They were from cattle fecal samples in Jixi City, Qiqihar City, Shuangyashan City, Heihe City, Yichun City, Suihua City, and Mudanjiang City, and one of them was from health sample.CRESS-DNA genome encodes replication-related proteins (Reps), mainly including Circoviridae, Genomoviridae, Smacoviridae, Geminiviri dae, Nanoviridae, and Bacilladnaviridae, but many members had not been classified.They widely exist in various samples (lakes, sewage or soil, etc.), plant samples, and terrestrial animal samples.The phylogeny of CRESS-DNA viruses was divided into two main branches.The sequence of CRESS-DNA virus identified in cattle fecal samples in this study was close to the genetic relationship of other species and had the potential for cross-species transmission (Fig. 12c).

DISCUSSION
In comparison to the strictly enclosed intensive farming modality employed for livestock like poultry and pigs, cattle farming involves both an intensive farming modality and a familial farming modality.The ranch was usually in a relatively open environment.Cattle were frequently exposed to human activities and environmental factors, and the pathogenic biological factors causing bovine diarrhea disease were also more complex.
The infection of pathogenic microorganisms and the change of intestinal flora and virome were the most important factors leading to the occurrence of bovine diarrhea.
Previous research had mainly concentrated on the investigation of diarrhoea-causing microorganisms in the context of single disease-related ecological factors (8,15,18).The research population was generally limited in size, and there was an absence of systematic disease ecological factors such as pathogen-host-environment. Due to the deficiency of microbial information concerning bovine diarrhea, the comprehensive risk factors of its occurrence have yet to be determined.Such as the type of pathogenic microorganism, the interaction between pathogenic microorganism and host factor, the interaction between pathogenic microorganism and environmental factor, and the interaction between host and environmental factor are yet to be fully understood.Despite ongoing research, the cause of bovine diarrhea remains unknown and existing strategies for its prevention and control are not as effective as hoped (8).For this study, the cattle herd in Heilongjiang Province was chosen as the research object.In their natural environment, fecal samples were collected from both cattle with diarrheic and healthy cattle.By considering the information from virology and bacteriology, a thorough evaluation of the major ecological risk factors of bovine diarrhea disease was conducted to uncover the features of virus and bacterial community present in the fecal samples of diarrheic and healthy cattle in various disease ecological backgrounds.
Investigating the correlation between the composition and abundance of virome and bacterial community and host and environmental factors, as well as the prevalence, genetic evolution, cross-species transmission, and pathogenicity of common bovine enteroviruses in cattle in relation to host and environmental factors was essential in order to gain a better understanding of the laws that govern them.For the first time, a large sample volume of virology and bacteriology studies has revealed the disease risk factors related to the occurrence of bovine diarrhea disease, providing a new concept for the formulation of efficient comprehensive prevention and control measures for bovine diarrhea disease.The term "microbiome" was first used as a convenient ecological framework for testing biological control systems, namely, the microbiome.This can be defined as a characteristic microbial community occupying a reasonably defined habitat that has unique physiological and chemical properties (19).At present, research on the human intestinal microbiota has proved that the microbiota participates in a series of physio logical processes that are critical to the host's health.The report showed that changes in the gastrointestinal microbiota were not only significantly related to human inflam matory intestinal diseases, but also to asthma, obesity, metabolic syndrome, cardiovas cular diseases, immune-mediated diseases, and neurodevelopmental diseases (19,20).Among the reported studies on the bovine microbiome, it was mainly focused on rumen microorganisms, including rumen microorganisms and feed digestion, changes in the rumen microbial composition with the growth of cattle age, and rumen microorgan isms and methane emissions (21)(22)(23).In a few studies on the bacteriology of cattle dung, Akkermansia, Solibacillus, Escherichia-Shigella, Alistipes, Solibacillus, Bacteroides, Prevotelaceae, and Bacillus were significantly enriched in the diarrhea samples (12,13).This study identified 39 bacterial phyla and 1,011 bacterial genera in fecal sam ples.In addition to the pathogenic bacteria identified in the previous studies, Entero coccaceae_Enterococcus and Clostridiaceae_Clostridium were identified in diarrhea fecal samples under different risk factors, and the important correlation of probiotics in health and diarrhea fecal samples was updated.Viromics research of other animals was mainly concerned with the identification of novel viruses (24) and the investigation of the cause of diseases (25).In the viromics study of other organ systems in cattle, the potential pathogenicity of influenza D virus (IDV) in the respiratory tract was determined through virus metagenomics identification (26).The BoAstV (BoAstV-CH13/NeoS1) and a new bovine double retrovirus, BoRV-CH15, were found in the samples of non-suppurative encephalitis in cattle.Five new CRESS-DNA viruses encoding replication-related proteins and three bovine parvoviruses have been identified in bovine plasma (26).In the macrogenomics sequencing study of cattle fecal samples, 26 viral families were identified and it was found that the reads of calicivirus and astrovirus in diarrhea calves were much higher than those in healthy calves (15,18).In this study, 39 virus families, 86 virus genera, and 110 virus species were identified in the fecal samples of cattle, which greatly enriched the virology study of cattle diarrhea fecal.In short, compared with other studies, the sample size in this study was large, and the types of bacteria and viruses identified were more complex.Not only that, but most of the other microbiological studies have only carried out viromics or bacteriomics studies, respectively.This was the first time that viromics had been combined with bacteriomics in fecal samples from cattle with diarrhea, comprehensively revealing and expanding the role of virology and bacteriology in diarrhea.The relationship between the virome at the family level and the bacterial community at the genus level and different factors of diarrhea and health was identified by making full use of the information of the bacterial community and the virome.The relationship between the changes of bovine gastrointestinal microflora and bovine diarrhea disease under different factors has been clarified in terms of bacteria and viruses.
Host microbiome plays an important role in host immunity, is at the interface between the host and pathogen, and affects disease outcomes.In recent years, accumulated research has shown that the intestinal microbiota is related to many diseases (27)(28)(29).Environmental and host factors affect the diversity of the intestinal flora.In many cases, the interaction between the host, the pathogen, and the environ ment was not sufficient to describe the disease dynamics in the field.The interaction between the host, a host microbiome, a pathogen, and the environment affects the occurrence of disease.In order to fully reveal the pathogenesis of diseases, it is necessary to transform the disease triangular model of host, pathogen, and environment into a disease tetrahedral model of host, host microbiome, pathogen, and environment.Bernardo-Cravo et al. (30) reported that environmental factors and the host microbiota affect the host-pathogen dynamics (30).By integrating environmental factors and microbiota into the pathogenesis of disease, we can improve our understanding of the disease dynamics and ecology (31).In our study, we have deeply analyzed the correlation between the virome and bacterial community and the host and environmental factors.The composition, abundance, and diversity of the viral and bacterial community in the diarrhea of cattle feces show different degrees of changes under different host and environmental factors.In cattle with diarrhea, the abundance of 16 virus species and 17 bacterial genera was significantly correlated with host factors (clinical status, type of cattle, and age of cattle) or environmental factors (aquaculture model and geographical location) (P < 0.05).In this study, under intensive farming conditions, the abundance of Coronaviridae and Astroviridae in the diarrhea of cattle fecal samples showed a high abundance, and the abundance of Piconaviridae, Coronaviridae, and Reoviridae in cattle diarrhea fecal samples also showed a high abundance.The correlation analysis between the identified virome and bacterial community and the environment or host indicates that the abundance of Picornaviridae, Coronaviridae, and Caliciviridae was significantly correlated with diarrhea (P < 0.05).BCoV in Coronaviridae has been proved to cause severe calf diarrhea.The Picornaviridae and Caliciviridae have been reported to be frequently detected in the diarrhea fecal samples in previous studies, but it has not been confirmed that they can independently cause diarrhea.In addition, this study showed that some potential diarrheic pathogens were significantly correlated with different environmental factors in the fecal samples of diarrhea; for example, the abundance of Astroviridae was significantly correlated with an intensive farming mode (P < 0.05), and the abundance of Circoviridae was significantly correlated with the non-intensive farming mode (P < 0.05).At the same time, some viruses were significantly correlated with host factors, and the abundance of Astroviridae and Reoviridae was significantly correlated with the calves (P < 0.05).Our research verified that various viruses in the real environment can cause diarrhea, a consequence that was influenced by both host and environmental factors.Additionally, this study revealed differences in the abundance of diarrhea-causing bacteria in diarrheic fecal samples under an aquaculture model.In intensive farming and dairy cow, there were many pathogenic bacteria in diarrheic fecal samples, such as Shigella and Enterococcaceae_Enterococcus has a high abundance and belongs to the dominant bacteria genus.However, the difference in the perform ance of diarrhea fecal samples in the non-intensive farming mode suggests that the pathogenic bacteria of bovine diarrhea were more likely to gather in the intensive farming or dairy cow, and that the pathogenic bacteria of diarrhea may cause more serious harm to the intensive farming dairy cow.It was evident from the data that the virus and bacteria associated with bovine diarrhea have an association with host factors such as clinical status, type of cattle, and age of cattle, as well as environmental factors such as an aquaculture model and geographical location.This study has yielded new information regarding the prevalence of bovine diarrhea disease and has provided us with advice on how to curb and combat the bovine diarrhea virus.In summary, the chi-squared test analysis display of this study revealed 10 viral families, including Virgaviridae, Totiviridae, Siphoviridae, Reoviridae, Picornaviridae, Herpesviridae, Genomovir idae, Coronaviridae, Caliciviridae, Astroviridae as well as the abundance of 17 bacterial genera of Mogibacterium, SMB53, Erysipelotrichaceae_Clostridium, Acidovorax, Actino myces, Lactococcus, Collinsella, Staphylococcaceae_Staphylococcus, Olsenella, Aerococ cus, Butyricicoccus, Chryseobacterium, Subdoligranulum, Carnobacterium, Trichococcus, Faecalibacterium, and Planomicrobium was significantly correlated with different host factors (clinical status and type of cattle) and environmental factors (aquaculture model) (P < 0.05), and the interaction network diagram appears, providing a new way of thinking about the occurrence of diarrhea disease.In contrast to past research, we have extensively examined the interplay between virome and bacterial community, distinct environments, and hosts, thereby augmenting our comprehension of the interaction between host, host microbial group, pathogen, and environment when bovine diarrhea disease occurs.
Disease is a complicated phenomenon that results from the interaction between a pathogen and the host.Research on human and animal diseases has verified that a synergistic pathogenic effect exists between bacteria and bacteria, viruses and viruses, and bacteria and viruses during the emergence of a disease (32,33).The complexity of the occurrence of diarrheic diseases was heightened by the interaction and synergism between intestinal microorganisms, and understanding this mechanism can help in the prevention and control of such diseases.In our research, we conducted a thorough investigation into the interplay between the discovered pathogens.A notable synergy exists between 12 pairs of viruses, 96 pairs of bacteria, and 22 pairs of bacteria and viruses (P < 0.05).In terms of the interaction between bacteria and bacteria, in fecal samples of diarrhea, Shigella, and Enterococcaceae_Enterococcus interact with each other (P < 0.05).In different aquaculture models, Clostridiaceae_Clostridium and Streptococcus interact with each other in the fecal samples of intensive breeding cattle diarrhea (P < 0.05).Shigella, Enterocacaee_Enterococcus, and Streptococcus interact synergistically (P < 0.05).Furthermore, Shigella interacted with Streptococcus in the fecal samples of non-intensive cattle diarrhea (P < 0.05).Among different types of cattle, in dairy cow diarrhea fecal samples, Enterococcaceae_Enterococcus and Shigella interacted synergisti cally (P < 0.05).Previous studies have reported that Porcine circovirus type 2 (PCV2) can promote the infection of other DNA viruses by inhibiting the induction of type I interferon (32), suggesting that the virus can interact with other viruses or promote the pathogenesis.It had also been reported that, when BoAstV was mixed with BRV or BToV infection, calves had more serious diarrhea in bovine enterovirus (34).In this study, a variety of potential bovine diarrhea viruses, including BKV, BToV, BoAstV, nebovirus, and calicivirus, were significantly co-pathogenic.Enterovirus and BToV, BoAstV BKV, and bocaparvovirus 1 were significantly co-pathogenic.In addition, this study found that there was a complex inter-boundary relationship between the viral and bacterial boundaries in the diarrhea cattle fecal samples.Previous studies have found that the interaction between bacteria and viruses can play a key role in the host-pathogen interface (35).Co-infection of a virus and bacteria may be recognized as a risk factor for the development of severe secondary diseases (36).Several reports have demon strated the existence of direct or indirect interactions between different intestinal pathogenic viruses and bacteria, thereby influencing their respective pathogenicity.For example, the direct binding of bacterial surface polysaccharides can enhance the stability of enterovirus particles and increase their adhesion to host receptors (33), the interaction between reovirus and bacteria enhances the thermal stability of the viruses (37), and infection by CagA-positive Helicobacter pylori induces expression of GII.4 norovirus attachment factors in nonsecretors' mucosa, expanding the host range of these strains (38).These strategies emphasize the cooperation mechanism between viruses and bacteria to realize mutual benefits through cross-generational interaction.In this study, multiple pathogenic bacteria causing diarrhea and potential viruses causing diarrhea showed synergistic pathogenic effects, such as Shigella and BoAstV, rotavirus A. Erysipelotrichaceae_Clostridium interacted with rotavirus A. Clostridiaceae_Clostridium interacted with coronavirus.The above research results well explain why the isolated pathogen does not occur in the infection experiment, and why we have not identified the "recognized pathogen" in some serious diarrhea diseases.The relevant data promote our further understanding of the interaction mechanism between pathogenic microor ganisms in the occurrence of bovine diarrhea.
In recent years, host intestinal factors, especially intestinal flora, have become a hot spot (39,40).In this study, under the background of comprehensive disease ecology, we found that the abundance of SMB53, Butyrivibrio, Facklamia, Trichococcus, and Turicibacter was significantly correlated with health (P < 0.05), with a trend of antagonizing diarrhea.Moreover, some probiotics and other pathogenic bacteria and viruses show complex interactions, such as the interaction between SMB53 and BKV.Butyrivibrio interacted with BToV and enterovirus.Turicibacter and Shigella, Enterococcaceae_Enterococcus, and Clostridiaceae_Clostridium interaction.Furthermore, different aquaculture models have varying interactions between pathogens and probiotics.Clostridiaceae_Clostridium interacted with Blautia and Butyricicoccus.In the case of non-intensive aquaculture model or beef cattle diarrhea fecal samples, Clostridiaceae_Clostridium interacted with Turicibacter and Bifidobacterium.This study found the synergy or antagonism between a variety of probiotics and bacteria and viruses, and the interaction was different under different environmental factors.Previous reports have shown that the intake of probiotics was related to better control of infectious diseases, and in some cases, the duration or severity of the infection can be improved (41,42).In view of the complex interaction between probiotics and intestinal bacteria in this study, we speculate that the interaction between probiotics and intestinal bacteria was also an important mechanism that affects the development of disease.In addition, probiotics, such as Collinsella and Bifidobaterium, have high abundance in many healthy fecal samples.In the beef cattle diarrhea fecal samples, Collinsella and Clostridiaceae_Clostridium also have antagonistic effects.At present, it is known that Collinsella is a probiotic in the intestinal tract that has the effect of inhibiting inflammation, inhibiting cell apoptosis, and anti-oxidation in the intestinal tract.The latest research has confirmed that Collinsella even plays a role in the inhibition of COVID-19, which may cause intestinal symptoms (43).Bifidobacterium also has many prebiotic functions in the human intestine, such as improving intestinal diseases caused by immune system disorders and inhibiting the invasion of pathogenic bacteria into the intestine (44).A variety of probiotics identified in this study were significantly related to health, and some of these probiotics exist in high abundance in healthy samples.These probiotics may be essential for sustaining the well-being of the bovine intestines and counteracting bovine diarrhea.Additionally, they could be used to create antiviral treatments in the future, which would be highly beneficial for the prevention and management of diarrhea diseases in cattle and healthcare.
Owing to the complexity of the bovine intestinal environment, there was a diver gence of views among researchers regarding the pathogenicity of intestinal pathogens.At present, it has been confirmed that BRV, BCoV, BVDV, and so on, can cause calf diarrhea.Other viruses that have been reported to may cause diarrhea include BoAstV, BToV, BoPV, BoNeV, and so on (9,45).Researchers have revealed that a variety of related viruses can be found in bovine diarrhea, yet the prevalence of bovine enteric viruses in masses remains uncertain (46)(47)(48).In our study, 10 kinds of bovine diarrhea-associated viruses, including BKV, BToV, BoNoV, BoNeV, BoPV, BRV, BoAstV, BCoV, BEV, and BVDV, were found in virome.A large-scale retrospective epidemiological survey was conducted for 10 kinds of bovine enteric viruses.The results showed that the total positive rate of the 10 kinds of bovine enteric viruses was 1.61-12.05%,and the positive rate of diarrhea fecal samples was 1.77-12.30%.Among them, the positive rates of diarrhea fecal samples of BRV, BCoV, and BVDV were 8.76% (89/1016), 12.20% (124/1016), and 3.15% (32/1016), respectively.Compared with previous research, the infection rate of BRV was slightly lower.This may be due to the large sample size of this study, and the fact that the sample collection includes adult cattle.However, in the further statistical analysis of infection rate, we found that the positive rate of BRV and BCoV detection in diarrhea fecal samples was significantly higher than that in healthy samples, and the analysis showed that the infection of BRV and BCoV was significantly correlated with diarrhea (P < 0.05), which was consistent with the previously reported clinical results that BRV and BCoV were the main pathogens causing bovine diarrhea.In addition, BRV was significantly correlated with the age of cattle (P < 0.05), and BCoV infection was significantly correlated with the aquaculture model (P < 0.05).The retrospective testing's statistical analysis outcomes exhibit conformity with the outcomes of metagenomic identification.The positive rate of BKV, BToV, BoNoV, BoPV, BoAstV, BoNeV, and BEV among other viruses that may cause enteritis ranges from 1.77% (18/1016) to 12.30% (125/1016), with BKV having the highest infection rate of 12.30% (125/1016).In addition, in the total 1,016 fecal samples of diarrhea, the positive rate of virus detection accounts for 48.33% (491/1016), while 16.54% (168/1016) samples detect at least two viruses, which proves that bovine viral diarrhea was widespread, and virus co-infection was common.BKV was also the main co-infection virus, which virome identification has confirmed interacted with a variety of viruses.Furthermore, BKV infection was found to be significantly related to the aquaculture model (P < 0.05).Results have demonstrated that BKV may be the most essential co-diarrhea virus in diarrhea fecal samples, and was a major factor in bovine diarrhea disease.Therefore, research related to BKV should not be overlooked.It was suggested that the environmental factors and microbial interaction should be taken into account when studying the pathogenicity of BKV.Additionally, BoAstV, a member of Astroviridae, has been gaining attention from researchers due to its potential for cross-species transmission (49).Numerous studies have shown that BoAstV is typically present in fecal samples from cattle with diarrhea, indicating a link between the virus and the disease (34,49).However, the current research on BoAstV has not clarified its ability to cause diarrhea.In this study, the positive rate of BoAstV detection in diarrhea fecal samples was 10.43% (106/1,016), which was higher than that in healthy samples, at 7.69% (8/104).The results of SPSS statistical analysis showed that the infection of BoAstV was significantly correlated with the aquaculture model and age (P < 0.05).In addition, BoAstV and various other bovine enteric viruses showed synergistic pathogenicity in fecal samples from diarrheic cattle.These data indicate that the pathogenicity of bovine astrovirus may also be affected by interactions among different hosts, environments, and even other intestinal microorganisms in clinical settings.The infection of other viruses, such as BToV, BoNeV, BoPV, and BEV, was also significantly related to the aquaculture model and cattle type.At the same time, this study identified that most viruses were also affected by the complex interaction of other intestinal bacteria and co-infected viruses.In this study, there was an interaction between viruses and bacteria, and even between different environmental and host factors.Their interaction presents different changes.The interaction of these factors brings strong complexity to the occurrence of diarrhea.In conclusion, our research shows that viral diarrhea plays an important role in bovine diarrhea.We must strengthen the prevention and control of known and confirmed pathogenic BCoV and BRV, and pay high attention to the problem of mixed infection, as well as the formation of synergy between these pathogens.In addition, we should pay attention to the impact of the aquaculture model on the occurrence of diarrhea.The non-intensive aquaculture model may be an effective way to avoid cattle diarrhea.In the intensive aquaculture model, and when calf diarrhea occurs, we should pay attention to the isolation breeding, and try to avoid the widespread occurrence of diarrhea caused by the mutual transmission of viruses.
Cattle were ubiquitous, varied in breed, abundant in quantity, and closely connected to human life.Cattle breeding was largely exposed, with frequent contact between people and other animals, which has caused multiple bovine zoonosis cases and posed a great danger to biosafety.There were also some viruses in bovine enteric viruses, such as BoAstV, BCoV, and BRV, which have shown potential for cross-species trans mission (45,50,51), and BCoV has even been reported to be isolated and identified in the fecal samples of children with diarrhea (52).Therefore, it was significant for biosafety to focus on cross-species, new virus mining, genetic evolution monitoring, the cattle farm biosafety prevention, and curbing the cross-species transmission of viruses at the front-end source.In this study, 191 complete genome sequences of 10 viruses in dairy cow fecal samples were identified through macro genome sequencing analysis of a large sample size, deep mining, and sequencing assembly, which greatly enriched the available complete virus genome database.Further, the genetic evolution of bovine diarrhea-associated viruses was comprehensively displayed through evolution ary analysis, and the phylogenetic analysis revealed the complexity and genetic diversity of the evolution of the important enteroviruses.The results showed that many kinds of viruses, such as BEV, BoAstV, and BoNoV, showed multiple branches of genetic evolution.BoAstV was also clustered with the buffalo astrovirus, the bovine respiratory astrovirus strain, and even the porcine astrovirus, which was of reference significance for analyzing the genetic evolution and even cross-species transmission of astroviruses.In addition, 70 other eukaryotic virus genomes were found in this study, of which 6 whole-genome sequences of the bovine hungarovirus 1 were identified.As far as we know, the bovine hungarovirus has only been detected in cattle in Hungary (53) and Turkey (54), and was found for the first time in dairy and beef cattle in China.In addition, CRESS-DNA viruses in the Circoviridae and Genomoviridae can infect a variety of eukaryotes, including mammals, birds, insects, fungi, and environmental samples.These viruses have high genetic diversity (55)(56)(57)(58)(59).The report shows that the infection of these viruses may play a role in the disruption of the host immune system and become an indirect factor in the causation of other diseases (60).Based on phylogenetic analysis, 62 CRESS-DNA virus genome sequences were found in the feces of cattle.The CRESS-DNA virus identified in this study has a close genetic relationship with many different species of hosts, indicating that these viruses have the potential to spread across species.In general, cattle may be an important host of many viruses, providing opportunities for cross-species transmis sion of viruses.
In conclusion, this study disclosed the composition of the virome and bacterial community in fecal samples of diarrheic cattle from Heilongjiang Province and their alterations under different host and environmental factors.There is a significant correlation between the prevalence of viruses and bacteria in cattle afflicted with diarrhea and various factors related to either the host (such as clinical status, age, and type of cattle) or the environment (such as the aquaculture model and geographical location).The coexistence of viruses-viruses, bacteria-bacteria, and bacteria-viruses in cattle with diarrhea leads to a significant synergistic effect.The 10 enteric viruses found in bovines exhibit a high incidence of infection, genetic variability, and coevolution across species in cattle with diarrhea.These viruses are linked to ecological factors that contribute to the development of disease, including the host and the environment.This study enriches the database of the bovine virome and bacterial community, and provides a new perspective for formulating comprehensive prevention and control measures of bovine diarrhea disease based on the ecological factors of the pathogen-host-environ ment system.

Sample collection and preparation
A total of 1,120 fecal samples from 1,120 cattle belonging to 58 farms were collected in Heilongjiang Province, China, between June 2020 and November 2020 (Table 1; Table S1).These 1,120 cattle included 1,016 diarrheic cattle and 104 healthy cattle.1,016 diarrheic cattle came from 34 dairy farms and 24 beef farms, covering 12 cities (regions) in Heilongjiang Province in northeast China, including Qiqihar, Daqing, Heihe, Jixi, Mudanjiang, Hegang, Harbin, Shuangyashan, Daxinganling, Yichun, Jiamusi, and Suihua.104 healthy cattle come from 4 dairy farms and 14 beef farms, covering nine cities (regions), including Hegang, Heihe, Daqing, Harbin, Daxinganling, Mudanjiang, Jixi, Yichun, and Jiamusi.The diarrheic cattle were diagnosed by licensed veterinarians.The diarrheic cattle showed clinical manifestations of acute diarrhea.Gloved fingers were used to collect fecal samples from the rectum of each cattle, and gloves were replaced between each cattle.Separate fecal samples were placed in sterile bags and immediately stored on ice for transport to the laboratory, then stored at −80°C until further process ing.
One thousand one hundred twenty samples were mixed into 72 sequencing samples, including 62 diarrhea fecal mixed samples (including 1,016 diarrhea fecal samples) and 10 healthy fecal mixed samples (including 104 healthy fecal samples).According to the disease ecological factors, 72 sequencing samples include different host factors: clinical status (diarrhea, health), cattle type (dairy cow: Holstein, beef cattle: Angus and Simmental), gender (male, female), cattle age (calves, adults), and environmental factors: aquaculture model (intensive, non-intensive), geographic location (eastern Heilongjiang, western Heilongjiang, southern Heilongjiang, northern Heilongjiang).The ecological background information of the diseases of the samples is shown in Table 1; Table S1.

Viral metagenomic sequencing
To achieve equivalent input amounts of raw feces, the total composite sample weight was targeted at 1 g using an electronic balance.Fecal samples were re-suspended in two volumes of phosphatebuffered saline (PBS) and vigorously vortexed for 15 min.The fecal supernatants were collected after centrifugation for 10 min at 15,000 × g.About 500 µL of each supernatant was filtered through a 0.45-µm Millipore filter to remove large cell-sized particles.The filtrates enriched in viral particles were treated with DNase and RNase to digest unprotected nucleic acid at 37°C for 60 min.The remaining total nucleic acid, protected from digestion within viral capsids, was extracted using the TIANamp Virus RNA Kit and TIANamp Stool DNA Kit (Tiangen Biotech Co., Ltd., Beijing, China) according to the manufacturer's instructions (61,62).The viral nucleic acid samples were subjected to reverse transcription reactions using reverse transcriptase and random hexamers, and subsequent second-strand DNA synthesis.Out of 72 sequencing samples, 70 libraries were successfully constructed using Nextera XT reagents (Illumina) and sequenced on the NovaSeq 6000 (Illumina).The Illumina sequencing and library construction were performed at the Shanghai Tanpu Biotechnol ogy Co., Ltd (Shanghai, China).Raw reads were filtered and trimmed by fastp (v0.20.0, https://github.com/OpenGene/fastp)to remove sequencing adapters and low-quality reads, including those reads scoredQ20 (Phred quality score of 20).Ribosomal RNAs and host reads subtraction by read-mapping were performed with BBMAP program (v38.51, https://sourceforge.net/projects/bbmap/files/).De novo genome assembly was performed using SPAdes v3.14.1 (63).These extracted assembled scaffolds limited the minimum contig length to 100 bases, with the best BLAST hits to NCBI nt database.In order to reduce the false positive mismatch of the reads caused by sequence homology, the mapped species with relative abundance higher than 1% could be conserved.The information of each viral library is shown in Table S2.To characterize the microbial communities, the heatmaps were generated using R (version 3.5.1,distance = "average, " scale = "row") with the pheatmap package.

Bacterial 16S rRNA sequencing
PCR amplification of the bacterial 16S rRNA genes V3-V4 region was performed using the forward primer 338F (5′-ACTCCTACGGGAGGCAGCA-3′) and the reverse primer 806R (5′-GGACTACHVGGGTWTCTAAT-3′).Samplespecific 7 bp barcodes were incorporated into the primers for multiplex sequencing.The PCR components contained 5 µL of buffer (5×), 0.25 µL of Fast pfu DNA Polymerase (5 U/µL), 2 µL (2.5 mM) of dNTPs, 1 µL (10 µM) of each Forward and Reverse primer, 1 µL of DNA Template, and 14.75 µL of ddH 2 O. Thermal cycling consisted of initial denaturation at 98°C for 5 min, followed by 25 cycles consisting of denaturation at 98°C for 30 s, annealing at 53°C for 30 s, and extension at 72°C for 45 s, with a final extension of 5 min at 72°C.PCR amplicons were purified with Vazyme VAHTSTM DNA Clean Beads (Vazyme, Nanjing, China) and quantified using the Quant-iT PicoGreen dsDNA Assay Kit (Invitrogen, Carlsbad, CA, USA).After the individual quantification step, amplicons were pooled in equal amounts, and pair-end 2 × 250 bp sequencing was performed using the Illumina MiSeq platform with MiSeq Reagent Kit v3 at Shanghai Personal Biotechnology Co., Ltd (Shanghai, China).Briefly, raw sequence data were demultiplexed using the demux plugin, followed by primers cutting with cutadapt plugin (64).Sequences were then quality filtered, denoised, merged, and chimera removed using the DADA2 plugin (65).The information of each bacterial library is shown in Table S4.

Virome and bacterial community analysis of fecal samples from diarrheaaffected and healthy cattle with various environment and host conditions
Analyses were performed to compare the constituents of fecal microbiome among different factors.To characterize the microbial communities, the heatmaps were generated using R (version 3.5.1,distance = "average' , " scale = "row") with the pheatmap package.The analysis of virome and bacterial community composition varying with latitude and longitude was performed by GraphPad Prism 8.

Correlations analysis of virome and bacterial community
The different factors (clinical status, aquaculture model, cattle type, cattle age, longitude, and latitude) were explored to identify covariates of virome and bacterial community variation, respectively, by calculating the association between continuous or categorical phenotypes and virus and bacteria abundance with multiple linear regression analysis and binary logistic regression analysis in IBM SPSS Statistics, version 22.0 (IBM SPSS Inc., USA).Various environment and host conditions included categorical variables: clinical status (diarrhea, health), aquaculture model (intensive, non-intensive), cattle type (dairy cow, beef cattle), breed (Angus, Holstein, Simmental), and continuous variables: cattle age ( ).The significance level in all analyses was 5%, with a confidence interval of 95%.The correlations of microorganisms (viruses or bacteria) with clinical status, aquaculture model, and cattle type were detected with 2 × 2 contingency tables and χ2 test.In order to measure the correlation between microbiome compo sition among different hosts and environmental factors, we performed virus-to-virus correlation or bacterial-to-bacterial correlation identification.The plot was generated using R software (v4.2.2) package "corrplot" (v0.92) and "ggplot2" (v3.4.2) through Hiplot Pro (https://hiplot.com.cn/).Descriptive statistics were calculated for virus or bacteria variables with various factors evaluated.Categorical data were analyzed with a χ2 test.Differences in values were considered statistically significant (P < 0.05) or highly significant (P < 0.01) and were included in chord diagram analysis.Correlations between gut virus and bacteria variation were calculated through the Spearman correlation test.Correlation coefficient was calculated, while statistical significance was determined for all pairwise comparisons.Only statistically significant correlations with correlation coefficients >0.26 were plotted.The correlation network was visualized via Cytoscape.

PCR confirmation and analysis of the 10 bovine enteric viruses
PCR confirmation was performed for the BKV, BoNoV, BoNeV, BoPV, BRV, BCoV, BToV, BoAstV, BEV, and BVDV in the 1,120 fecal samples in our study.The primer sequences are listed in Table 3. RT-PCR conditions were the same as described in the corresponding references.The detection results of BCoV had been reported in a previous study (66).The positive rate of 10 viruses in Heilongjiang Province was carried out using GraphPad Prism 8.00 (GraphPad Software, Inc., USA).The map of Heilongjiang Province was drawn with an online map-generation website (http://pixelmap.amcharts.com/)(67).Co-infections of identified viruses were performed using the online software Hiplot (https://hiplot-aca demic.com/)(68).The different factors were explored to identify covariates of virome variation, respectively, by calculating the association between continuous or categorical phenotypes and 10 bovine enteric viruses with binary logistic regression analysis in IBM SPSS Statistics, version 22.0 (IBM SPSS Inc., USA).The positive rate between different factors for 10 bovine enteric viruses was performed by GraphPad Prism 8.00.

Sequence alignment and phylogenetic analyses
The nucleotide (nt) and deduced amino acid (aa) sequences were analyzed using the MegAlign software (DNASTAR, Madison, WI, USA) (77).The alignment results were analyzed by using the MegAlign tool in the Lasergene DNASTAR version 5.06 software (DNASTAR Inc., USA).The nt sequences of the genomes and the deduced aa sequences of the ORFs were compared to those of representative members of related viral species or genera using ClustalW by multiple sequence alignment.Phylogenetic trees were constructed using the maximum likelihood (ML) method and a bootstrap value of 1,000 by MEGA 6 program.The alignment sequences were constructed as Bayes evolutionary trees using MrBayes 3.2.7 program with set cutoff frequency (default value 0.10).We used the "sump" and "sumt" commands to get more detailed diagnostic information after the run has completed.The phylogenetic tree was annotated with the Interactive Tree Of Life (iTOL) software (http://itol.embl.de/),an online tool for the display and annotation of phylogenetic trees (78).

Statistical analysis
Statistical analysis was performed in R (version 3.5.1).Unless otherwise stated, analysis was performed using a Wilcoxon test to compare two groups and Kruskal-Wallis for more than two groups.Statistical significance is represented by *P value < 0.05, **P value < 0.01, ***P value < 0.001, and ****P value < 0.0001.BEV-R GATTAGCAGCATTCACGGC

FIG 1
FIG 1 The composition of virome at the family level and bacterial community at the genus level detected in cattle fecal samples.(a) Composition of virome in the whole, diarrhea, and healthy samples (from the inner to the outer circle).(b) Composition of virome in cattle fecal samples in 12 cities (regions) of Heilongjiang Province.From the inner circle to the outer circle were Suihua, Shuangyashan, Heihe, Harbin, Qiqihar, Mudanjiang, Daxinganling, Hegang, Yichun, Jixi, Jiamusi, and Daqing, respectively.(c) Composition of the virome in cattle fecal samples under various factors.From the inner circle to the outer circle were diarrhea-intensive, health-intensive, diarrhea-non-intensive, health-non-intensive, diarrhea-Holstein, health-Holstein, diarrhea-Simmental, health-Simmental, diarrhea-Angus, and health-Angus.(d) Composition of bacterial community in the whole, diarrhea, and healthy samples (from the inner to the outer circle).(e) Composition of bacterial community in cattle in 12 cities (regions) of Heilongjiang Province.Inner to outer is the same as panel b.(f) Composition of bacterial community in cattle fecal samples under various factors.Inner to outer is the same as panel c.

FIG 2
FIG 2 Heatmap abundance analysis of virome at the family level in cattle fecal samples.(a) Heatmap abundance analysis of virome in diarrheic and healthy cattle fecal samples.(b) Heatmap abundance analysis of virome in diarrheal fecal samples under intensive and non-intensive farming.(c) Heatmap abundance analysis of virome in diarrheal fecal samples from dairy cows and beef cattle.(d) Heatmap abundance analysis of virome in diarrheic and healthy cattle fecal samples under intensive farming.(e) Heatmap abundance analysis of virome in diarrheic and healthy cattle fecal samples under non-intensive farming.(f) Heatmap abundance analysis of virome in diarrheic-and healthy-dairy cow fecal samples.(g) Heatmap abundance analysis of virome in diarrheic-and healthy-beef cattle fecal samples.

FIG 3
FIG 3 Heatmap abundance analysis of bacterial community at the genus level in cattle fecal samples.(a) Heatmap abundance analysis of the bacterial community in diarrheic and healthy cattle fecal samples.(b) Heatmap abundance analysis of the bacterial community in cattle diarrheic fecal samples under intensive and non-intensive farming.(c) Heatmap abundance analysis of the bacterial community in dairy cow and beef cattle diarrheic fecal samples.(d) Heatmap abundance analysis of bacterial community in diarrheic and healthy cattle fecal samples under intensive farming.(e) Heatmap abundance analysis of the bacterial community in diarrheic and healthy cattle fecal samples under non-intensive farming.(f) Heatmap abundance analysis of the bacterial community in diarrheic-and healthy-dairy cow fecal samples.(g) Heatmap abundance analysis of the bacterial community in diarrheic and healthy beef cattle fecal samples.

FIG 4
FIG 4 Relative abundance of virome at the family level and bacterial community at the genus level with longitude and latitude variations in cattle diarrheic fecal samples.(a) Relative abundance of virome with longitude and latitude variations.(b) Relative abundance of bacterial community with longitude and latitude variations.

FIG 5
FIG 5 Effect size of various factors on the abundance of virome at the family level and bacterial community at the genus level in cattle fecal samples.(a) The effect size of clinical status on cattle virome variation.(b) The effect size of aquaculture model on cattle virome variation.(c) The effect size of cattle type on cattle virome variation.(d) The effect size of age on cattle virome variation.(e) The effect size of longitude on cattle virome variation.(f) The effect size of latitude on cattle virome variation.(g) The effect size of clinical status on cattle bacterial community variation.(h) The effect size of aquaculture model on cattle bacterial community variation.(i) The effect size of cattle type on cattle bacterial community variation.(j) The effect size of age on cattle bacterial community variation.(k) The effect size of longitude on cattle bacterial community variation.(l) The effect size of latitude on cattle bacterial community variation.

FIG 6
FIG 6 Intra-kingdom correlation analysis in the virome at species level and bacterial community at the genus level under various factors.(a-f) Intra-kingdom correlation analysis of virome in cattle fecal samples under various factors.(c-f) Intra-kingdom correlation analysis of virome in cattle diarrheic fecal samples under various factors.(g-l) Intra-kingdom correlation analysis of bacterial community in cattle fecal samples under various factors.(i-l) Intra-kingdom correlation analysis of bacterial community in diarrheic fecal samples from cattle under various factors.Statistical analysis was performed using Spearman correlation analysis.

FIG 7
FIG 7 Correlation analysis of virome at the family level and bacterial community at the genus level in cattle diarrheic fecal samples with various factors.

FIG 8
FIG 8 Intra-boundary and inter-boundary correlations between the virome at the species level and the bacterial community at the genus level in diarrheic fecal samples from cattle.Correlation coefficient was calculated, while statistical significance was determined for all pairwise comparisons.Only statistically significant correlations with |correlation coefficient| > 0.26 were plotted.The correlation network was visualized via Cytoscape.The color intensity of the interspecies connective line was proportional to the correlation coefficient, where blue lines indicate inverse correlations and red lines indicate positive correlations.

FIG 9 FIG 10
FIG 9 Positive rate and geographical distribution of 10 bovine enteric viruses.(a) Positive rate of 10 bovine enteric viruses.(b) Co-infection of 10 bovine enteric viruses.(c) Geographical distribution of 10 bovine enteric viruses.

FIG 12
FIG 12 Phylogenetic analysis of other eukaryotic viruses.(a) Phylogenetic analysis based on complete genome of Picornaviridae strains.(b) Phylogenetic analysis based on complete genome of Dicistroviriade strains.(c) Phylogenetic analysis based on Rep protein of CRESS-DNA virus strains.

TABLE 1
Summary table of sample collection and classification

TABLE 3
The primers used in this study